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Deep Convolutional Neural Networks for the Prediction of Molecular Properties: Challenges and Opportunities Connected to the Data
We present a flexible deep convolutional neural network method for the analysis of arbitrary sized graph structures representing molecules. This method, which makes use of the Lipinski RDKit module, an open-source cheminformatics software, enables the incorporation of any global molecular (such as m...
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| Pubblicato in: | J Integr Bioinform |
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| Autori principali: | , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
De Gruyter
2018
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6798861/ https://ncbi.nlm.nih.gov/pubmed/30517077 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1515/jib-2018-0065 |
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